---
title: SambaNovaCloudEmbeddings
---

This will help you get started with SambaNovaCloud embedding models using LangChain. For detailed documentation on `SambaNovaCloudEmbeddings` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).

**[SambaNova](https://sambanova.ai/)'s** [SambaNova Cloud](https://cloud.sambanova.ai/) is a platform for performing inference with open-source models

## Overview

### Integration details

| Provider | Package |
|:--------:|:-------:|
| [SambaNova](/oss/integrations/providers/sambanova/) | [langchain-sambanova](https://python.langchain.com/docs/integrations/providers/sambanova/) |

## Setup

To access ChatSambaNovaCloud models you will need to create a [SambaNovaCloud](https://cloud.sambanova.ai/) account, get an API key, install the `langchain_sambanova` integration package.

```bash
pip install langchain-sambanova
```

### Credentials

Get an API Key from [cloud.sambanova.ai](https://cloud.sambanova.ai/apis) and add it to your environment variables:

``` bash
export SAMBANOVA_API_KEY="your-api-key-here"
```

```python
import getpass
import os

if not os.getenv("SAMBANOVA_API_KEY"):
    os.environ["SAMBANOVA_API_KEY"] = getpass.getpass("Enter your SambaNova API key: ")
```

If you want to get automated tracing of your model calls you can also set your [LangSmith](https://docs.smith.langchain.com/) API key by uncommenting below:

```python
# os.environ["LANGSMITH_TRACING"] = "true"
# os.environ["LANGSMITH_API_KEY"] = getpass.getpass("Enter your LangSmith API key: ")
```

### Installation

The LangChain SambaNova integration lives in the `langchain-sambanova` package:

```python
%pip install -qU langchain-sambanova
```

## Instantiation

Now we can instantiate our model object and generate chat completions:

```python
from langchain_sambanova import SambaNovaCloudEmbeddings

embeddings = SambaNovaCloudEmbeddings(
    model="E5-Mistral-7B-Instruct",
)
```

## Indexing and Retrieval

Embedding models are often used in retrieval-augmented generation (RAG) flows, both as part of indexing data as well as later retrieving it. For more detailed instructions, please see our [RAG tutorials](/oss/langchain/rag).

Below, see how to index and retrieve data using the `embeddings` object we initialized above. In this example, we will index and retrieve a sample document in the `InMemoryVectorStore`.

```python
# Create a vector store with a sample text
from langchain_core.vectorstores import InMemoryVectorStore

text = "LangChain is the framework for building context-aware reasoning applications"

vectorstore = InMemoryVectorStore.from_texts(
    [text],
    embedding=embeddings,
)

# Use the vectorstore as a retriever
retriever = vectorstore.as_retriever()

# Retrieve the most similar text
retrieved_documents = retriever.invoke("What is LangChain?")

# show the retrieved document's content
retrieved_documents[0].page_content
```

## Direct Usage

Under the hood, the vectorstore and retriever implementations are calling `embeddings.embed_documents(...)` and `embeddings.embed_query(...)` to create embeddings for the text(s) used in `from_texts` and retrieval `invoke` operations, respectively.

You can directly call these methods to get embeddings for your own use cases.

### Embed single texts

You can embed single texts or documents with `embed_query`:

```python
single_vector = embeddings.embed_query(text)
print(str(single_vector)[:100])  # Show the first 100 characters of the vector
```

### Embed multiple texts

You can embed multiple texts with `embed_documents`:

```python
text2 = (
    "LangGraph is a library for building stateful, multi-actor applications with LLMs"
)
two_vectors = embeddings.embed_documents([text, text2])
for vector in two_vectors:
    print(str(vector)[:100])  # Show the first 100 characters of the vector
```

## API Reference

For detailed documentation on `SambaNovaCloud` features and configuration options, please refer to the [API reference](https://docs.sambanova.ai/cloud/docs/get-started/overview).
